Newsletter

What role do you play at COSMOS?

At COSMOS, I work as a Data Engineer, primarily contributing to Research Engineering at the intersection of data collection, data engineering, and AI. My work involves building and supporting the systems that power research workflows and enable large-scale analysis. Beyond engineering, I’m also part of the design team, where I create visual assets such as newsletter headers and social media graphics that help communicate COSMOS’s work to a broader audience.

Please share a bit about your professional background and experience.

I recently completed my Master’s degree in Computer Science from the Illinois Institute of Technology and have experience spanning AI systems, data engineering, and scalable application development. I’ve worked in security engineering, co-founded two startups, and built data pipelines, AI-powered platforms, and agent-based systems focused on solving real-world problems. At COSMOS, I’m excited to apply data-intensive and AI-driven approaches to study complex social systems and contribute to research that creates meaningful impact.

What attracted you to join the COSMOS Center? What aspects of COSMOS’ vision, mission, and culture stood out to you, and why?

What attracted me to COSMOS was the opportunity to work on research that extends beyond building technology for its own sake and instead addresses real societal challenges. Prof. Agarwal’s vision of using computational methods and AI to understand complex online behaviors, information ecosystems, and emerging cognitive threats strongly resonated with me because it combines rigorous research with meaningful impact.

How do you anticipate your role at COSMOS helping your growth on both a personal and professional level? Are there any specific skills or experiences you’re looking to gain?

I believe my role at COSMOS will help me grow both as an engineer and as an individual. I’m excited to strengthen my skills in research engineering while gaining deeper exposure to systems and network engineering, particularly the server infrastructure that powers large-scale research. I also look forward to learning from an interdisciplinary team and broadening my perspective through collaboration and continuous learning.

From your experience, what tips, insights, or advice would you share with someone starting a new role at COSMOS?

I would say it would be to have a strong grasp of the fundamentals. The work at COSMOS is Integrative and constantly evolving, so being curious, adaptable, and willing to learn goes a long way. A solid foundation in the basics makes it much easier to understand new concepts, collaborate effectively, and make meaningful contributions from the start.

If you could share a meal with any historical figure or fictional character, who would it be and what would you want to talk about and want to learn from them?

Even though not historical, it would have to be Hideo Kojima. I’ve always admired how he pushed the boundaries of what video games could be by blending cinematic storytelling with interactive experiences. His journey is equally inspiring. From facing rejection early in his career to building his own game studio and creating globally influential titles through sheer conviction and creativity. I’d love to talk to him about how he approaches innovation, balances artistic vision with execution, and remains resilient in the face of uncertainty. I think there is a lot to learn from someone who continuously redefines the limits of their craft without compromising on their ideas.

COSMOS Research Center continues to push the boundaries of socio-computational research, unveiling two groundbreaking studies at the prestigious 14th International Conference on Complex Networks and their Applications (Complex Networks), New York, USA. This conference brings together global leaders in computer science and network modeling to address the world’s most pressing digital threats.

The first study, entitled “Competing Narratives on TikTok: Modeling Taiwan’s 2024 Election Dynamics,” examines how conflicting storylines spread and compete, using empirical data from TikTok during Taiwan’s 2024 presidential election. Introducing a novel stance-aware epidemiological framework, the authors categorized users based on whether they actively promote, oppose, or remain skeptical of a narrative. By modeling this ideological resistance, the authors achieved significantly higher predictive accuracy compared to classical and non-stance-aware contagion tracking models. Overall, the results demonstrate that incorporating user stance significantly improves the prediction of narrative virality, revealing that raw transmission speed and the time it takes for users to shift their beliefs are the true engines of online spread.

The second study, entitled “Narrative Diffusion in Social Topologies: A Comparative Study of LLM-Driven Dynamics,” investigates how geopolitical narratives regarding the Russia-Ukraine conflict diffuse and transform across synthetic social networks using Generative AI models. Using large language models like GPT-4o and Gemini 2.5 Pro, the analysis shows that a network’s underlying “shape” dictates a narrative’s fate: scale-free networks (hub-heavy platforms) maximize a story’s reach while keeping the original message highly stable, whereas small-world networks (tight-knit communities) restrict reach but heavily mutate the narrative’s meaning. The study also reveals that Gemini promotes greater creative transformation of content, whereas GPT-4o acts conservatively and favors stability. Overall, the findings suggest that the joint role of network structure and generative AI behavior critically shapes digital information flows, offering a new framework for studying AI-mediated information operations.

Both studies leverage advanced computational approaches to analyze digital communication, but they differ in focus and methodological emphasis. The narrative diffusion study applies advanced large language models to synthetic environments to interpret how content mutates as it flows through different network topologies. In contrast, the TikTok study employs mathematical epidemiological modeling against real-world video data to map how the ideological stances and active pushback of human users shape the trajectory of competing storylines.

Together, these studies demonstrate the power of social computing in uncovering nuanced patterns in digital communication, whether through structural network simulations or stance-aware user tracking. Scientifically, they advance methods for integrating generative AI behaviors and complex human belief states into information diffusion frameworks; societally, they offer actionable insights for platform governance, providing clear evidence that structural interventions like algorithmic throttling and rapid fact-checking are essential to mitigate harmful online campaigns.

COSMOS continues to advance global scholarship in computational social science and network analytics with a comprehensive new survey published in the Journal of Social Network Analysis and Mining, a leading Springer Nature journal. This research examines the emerging field of “Computational Narratology“, which tracks how emotionally charged, structured stories propagate and evolve across online networks. Unlike traditional information diffusion models that treat social media posts as static, atomic units, this survey highlights the need for frameworks that capture the dynamic, interpretive nature of human storytelling. By synthesizing almost eight decades of interdisciplinary scholarship, the research introduces a detailed taxonomy of computational models, including narrative tracking models, role-based event chains, multimodal variational methods, and stance-aware epidemic adaptations, capable of tracking how complex storylines mutate, change in stance, and cross platform boundaries.

Key findings reveal that traditional information diffusion models fall short when capturing “semantic drift”, the phenomenon where stories continuously shift in tone, framing, and intent as they are remixed or satirized across polarized communities. The research demonstrates that modern narratives are inherently multimodal and multilingual, revealing that accurate tracking requires new cross-modal reasoning frameworks to follow storylines that blend text, memes, videos, and emojis while migrating across vastly different platform ecosystems.

The research highlights how narrative spread online is not merely the passive transmission of data but an active process where users act as co-authors, reshaping stories through collective interaction patterns and shared cultural resonance. By identifying critical gaps such as the lack of annotated narrative datasets, platform data restrictions, and the heavy use of sarcasm and figurative language, the survey establishes a methodological foundation for capturing the nuanced dynamics of online storytelling, offering a powerful tool for analyzing public discourse and identifying coordinated narrative manipulation.

This work underscores COSMOS’s leadership in AI-powered social media analytics, combining computational social science, network modeling, and ethical AI to solve pressing real-world problems. By publishing in Springer’s Journal of Social Network Analysis and Mining, COSMOS continues to contribute to international scholarship on narrative evolution, collective sensemaking, and the resilience of online ecosystems. Read the full article here.

COSMOS Research Center at the University of Arkansas at Little Rock is proud to highlight U.S. Senator John Boozman’s continued support for its research in online behavior, social media analytics, and national security. The support recognizes COSMOS’s role in advancing research that helps address emerging challenges in the digital information environment, including disinformation, foreign influence operations, and cognitive threats.

The online behavioral research conducted by COSMOS is so important now because of all the disinformation in the world,” U.S. Senator John Boozman said. “I am pleased to support UA Little Rock and its Collaboratorium for Social Media and Online Behavioral Studies. This award recognizes the significance of this program to our national security,” Senator Boozman said. “The important research conducted here will enhance our ability to counter the use of novel social media tactics by foreign extremists and terrorist groups threatening the United States and our allies.”

Through its interdisciplinary research, COSMOS studies how information spreads across online networks and develops analytical tools to detect, model, and understand coordinated digital behavior. This work contributes to national defense and security efforts by strengthening capabilities to identify and respond to adversarial narratives, influence campaigns, and emerging threats across social media platforms.

“We are extremely grateful for the support from UA Little Rock leadership and U.S. Senator John Boozman for championing this vital research,” Prof. Nitin Agarwal said. “This funding will help COSMOS Research Center to continue to develop analytical tools and capabilities to strengthen our national defense and security apparatus against cognitive threats.”

Prof. Agarwal continued, “The support allows us to deepen our research impact while preparing students to lead in high-demand intelligence and analytics fields.”

What role do you play at COSMOS?
I am a Graduate Research Assistant at the COSMOS Research Center.

Please share a bit about your professional background and experience.
I am a Master’s student in Information Science at the University of Arkansas at Little Rock and a Graduate Research Assistant at the COSMOS Research Center. I bring more than seven years of experience in software engineering, with a focus on building software products for the financial technology sector.

My professional background includes contributing to high-scale open banking systems and AI-powered identity verification infrastructure for enterprise businesses. My experience spans software engineering, product management, machine learning research, and cloud engineering.

What attracted you to join the COSMOS Center? What aspects of COSMOS’s vision, mission, and culture stood out to you, and why?
I was excited to work with Prof. Nitin Agarwal on artificial intelligence and social computing research because of the strong alignment between my background in software engineering and his pioneering work at COSMOS in social media and narrative analysis.

My experience has centered on solving real-world problems through software systems, and COSMOS provides an opportunity to apply that problem-solving mindset to impactful AI research. Prof. Agarwal’s vision for using computational methods to understand complex online behaviors and digital information ecosystems strongly aligns with my interests in AI engineering and applied research.

How do you anticipate your role at COSMOS helping your growth on both a personal and professional level? Are there any specific skills or experiences you’re looking to gain?
Through my role at COSMOS, I hope to further expand my technical and research capacity in AI engineering while contributing to the design, implementation, and study of AI-driven software systems.

Prof. Agarwal is a well-recognized global research leader in AI and social computing, and working with him allows me to stretch toward a global-first research mindset. I look forward to learning how to apply my software engineering and problem-solving skills to artificial intelligence research that addresses societal challenges.From your experience, what tips, insights, or advice would you share with someone starting a new role at COSMOS?
My advice would be to keep an open mind and do the work needed to build capacity. COSMOS is a place where learning, research, and real-world problem-solving come together, so being open to new ideas and willing to grow is very important.

If you could share a meal with any historical figure or fictional character, who would it be, and what would you want to talk about and want to learn from them?
I would like to share a meal with Nelson Mandela. I would like to ask him how he stayed focused and relentless in the midst of a difficult situation where the personal reward was not immediately visible. I believe there is a lot to learn from his patience, resilience, and commitment to a larger purpose.

In this month’s research spotlight, COSMOS highlights its leadership in redefining digital media as an active force in shaping human behavior through three studies presented at the 14th International Conference on Complex Networks and their Applications (Complex Networks) in New York, USA. These studies examine the complex relationship between digital platforms, their algorithms, and content creators. While these research efforts all utilize large-scale auditing of YouTube content, they differ in their focus: one examines how platform algorithms shape user activity levels by pushing moderate-intensity content, while the others analyze how creators maintain internal consistency and coherence within their own channels.

The first study, “ViMET-R: Auditing Activity-Level Bias in YouTube Shorts Recommendations,” introduces ViMET-R, an advanced AI technique that visually estimates the physical energy expenditure of activities shown in videos using Metabolic Equivalent of Task (MET) scores.. By applying this model to 84,816 YouTube Shorts, the research uncovered a consistent pattern where the recommendation algorithm converges toward moderate-intensity content, regardless of the user’s initial viewing behavior. This systematic activity-level bias introduces a new dimension to algorithmic drift, suggesting that platforms may be shaping user behavior in ways that go beyond traditional content personalization.

The second study, “Uncovering Channel -Level Behaviors via Multimodal Characterization in YouTube Content,” presents a combined visual and text-based framework for characterizing YouTube channels by comparing similarity across five key features: titles, descriptions, transcripts, categories, and the video’s color palette. By evaluating 14,000 videos from 136 channels, the researchers identified three distinct editorial patterns: “Mild Visual Consistency, High Textual Variability”, “Category-Stable Channels”, and “Loosely Structured but Topically Focused Channels”. The findings demonstrate a scalable and language-independent method for uncovering stable channel groupings based on both visual and semantic features. This work offers a new lens for auditing channel-level behavior and understanding the patterns that shape long-term content strategies.

The third study, “Characterizing YouTube Channels Through Semantic Consistency Across Content Features,” introduces a content-based framework to analyze how YouTube channels communicate their identity through the consistent use of titles, descriptions, transcripts, and categories. By calculating how closely the text aligns (semantic similarity) across over 157,000 videos from 150 channels, the study identified three distinct editorial patterns: “Diverse-Format Channels”, “Label-Stable, Content-Variable Channels”, and “Structurally Cohesive Channels”. The findings reveal significant differences in how creators manage their messaging and presentation strategies over time.

These studies collectively imply that digital media is no longer just a passive platform but an active force in shaping human behavior and creator identity. These studies provide essential tools for auditing algorithmic fairness and understanding the long-term strategies that influence the information and physical activity levels we are exposed to daily.

COSMOS continues to engage with the growing public health challenge of adverse drug events, particularly drug-drug interactions (DDIs) that arise when multiple medications are taken concurrently. Our recent publication in Scientific Reports, published by Nature Portfolio, introduces a new model, the Protein Sequence-Structure Similarity Network (PS3N), for detecting dangerous drug-drug interactions (DDIs). Traditionally, it has been hard to predict these risky drug combinations because standard clinical trials cannot test every possible mix of medicines, especially over long periods or across diverse groups of people. Existing tools only rely on surface-level data like chemical traits or patient reports to make predictions. This research, however, introduces a deeper perspective by focusing on the underlying biology of how medications work.

Recently published in the journal Scientific Reports by Nature Portfolio, the study introduces the Protein Sequence-Structure Similarity Network (PS3N) to fix this problem. This model is the first to directly integrate both the genetic blueprints (called protein sequences) and the biological structures (called 3D protein structures) of drug targets to predict potential drug risks. By analyzing these fundamental biological building blocks, the model captures subtle molecular mechanisms that traditional methods often overlook.

The impact of this approach is demonstrated through its remarkable accuracy and real-world discovery. In rigorous testing across multiple datasets, key findings reveal that the model achieved up to 98% precision and 95% accuracy and successfully identified 297 entirely new drug interactions that have never been reported in existing clinical literature. 

The study further highlighted unseen dangers among these newly discovered risks, identifying that a common acne cream could potentially interact with treatments for serious conditions like heart disease or glaucoma. It also flagged unexpected risks when mixing certain mental health medications with treatments to help people quit smoking. By bringing these hidden biological connections to light, PS3N provides a powerful and reliable way to ensure safe and effective treatment outcomes for patients. Click here to read the full article.

We are proud to share that Prof. Nitin Agarwal, Founding Director of the COSMOS Research Center, Jerry L. Maulden-Entergy Endowed Chair, and Donaghey Distinguished Professor of Information Science at the University of Arkansas at Little Rock, has been named the recipient of the 2026 UALR Faculty Excellence Award in Research and Creative Works.

This prestigious recognition highlights Prof. Agarwal’s exceptional research leadership and sustained contributions to social computing, artificial intelligence, cognitive security, and online behavioral analysis. Since joining UA Little Rock in 2009, he has built an internationally recognized interdisciplinary research program examining how information spreads across online networks and how digital influence campaigns shape public perception.

Through COSMOS, Prof. Agarwal has advanced pioneering research on modern information platforms, digital influence operations, social cyber forensics, and AI-enabled approaches for understanding complex online behaviors. His work has supported the development of analytical frameworks and tools for detecting, modeling, and mitigating adversarial influence campaigns, online scams, and emerging cognitive threats.

Over the past five years, Prof. Agarwal has secured more than $60 million in federal funding, including support from the National Science Foundation, DARPA, and the U.S. Department of Defense, with nearly $30 million directly supporting UA Little Rock research initiatives. His collaborations span more than 200 researchers across 130 academic, government, and industry organizations worldwide. His scholarly contributions include 12 books, more than 400 articles in top-tier journals and conferences, and 26 best paper awards.

Reflecting on the honor, Prof. Agarwal stated, “I am deeply honored to receive the University of Arkansas at Little Rock Faculty Excellence Award for Research and Creative Endeavors. I extend my sincere gratitude to the university leadership, the Board of Trustees, the Board of Visitors, and the award sponsors for this meaningful recognition.”

He added, “This recognition is both motivating and reaffirming, strengthening my commitment to advancing meaningful work aligned with our shared mission. I am profoundly grateful to my mentors, as well as the current and former students and staff at COSMOS at UALR, whose dedication and support have been instrumental.”

Prof. Agarwal received this university-level recognition for the third time (i.e., 2015, 2021, 2026). This achievement underscores his continued leadership in advancing impactful interdisciplinary research at the intersection of AI, social computing, and cognitive security.

What role do you play at COSMOS?

I am a graduate research assistant at COSMOS, and I am helping Prof. Nitin Agarwal with projects on narrative analysis and content traps.

Please share a bit about your professional background and experience.

I am originally from Nepal. I completed my bachelor’s degree in Electronics, Communication, and Information Engineering from the Institute of Engineering, Thapathali Campus, Tribhuvan University. After graduation, I worked as a Course Instructor and Visiting Lecturer, teaching subjects such as C programming, Microprocessor Architecture, and Big Data Technologies at different colleges, including my own campus. Currently, I am studying Information Science at the University of Arkansas at Little Rock, with a focus on AI, network science, and research in computational social systems.

What attracted you to join COSMOS Research Center? What aspects of COSMOS stood out to you, and why?

While working as a Course Instructor, I felt there was more in me than just delivering core programming concepts and computer knowledge. Slowly, the research interest grew as the technology advanced rapidly with the development of LLMs. I was drawn to Prof. Nitin Agarwal’s research because it combines rigorous network science with real-world social impact, especially in understanding influence, misinformation, and digital ecosystems. His vision at the COSMOS Research Center for building scalable, interdisciplinary, and socially responsible computational methods strongly aligns with my interests in Machine Learning, AI,  and LLMs. I’m particularly inspired by the collaborative, research-intensive culture Prof. Agarwal has fostered that encourages innovation and continuous learning.

How do you anticipate your role at COSMOS helping your growth on both a personal and professional level? Are there any specific skills or experiences you’re looking to gain?

As a graduate research assistant under Prof. Agarwal, I look forward to being deeply involved in every stage of the research process, from systematic literature review and experimental design to large-scale data analysis and article writing. My goal is to grow into an independent researcher by strengthening both my technical skills in network science and AI, and my theoretical grounding in computational social science. I also want to improve how I communicate complex findings clearly and responsibly, both in academic publications and broader research discussions.

From your experience, what tips, insights, or advice would you share with someone starting a new role at COSMOS?

From my experience, I would say come in ready to learn and take initiative. COSMOS is a very professional and collaborative environment where everyone is working on real-world research problems, so being proactive and responsible really matters. Prof. Agarwal has high standards and a clear research vision, and hence, there is a lot to learn just by observing how he thinks and works. Also, make the most of the team around you; people here are supportive, knowledgeable, and always willing to help if you are open to learning.

If you could share a meal with any historical figure or fictional character, who would it be, and what would you want to talk about and want to learn from them?

If I could share a meal with someone, I would choose Mahendra Singh Dhoni, a world-renowned and highly accomplished cricketer from India. As someone who enjoys playing cricket, I’ve always admired his calm mindset and decision-making under pressure. I would love to talk to him about how he reads the game, stays composed in high-stakes moments, and makes strategic decisions for the team. I think there’s a lot to learn from that kind of leadership and clarity, especially in research where patience, strategy, and teamwork also matter.

In this month’s research spotlight, COSMOS at UA Little Rock highlights its leadership in socio-computational research through three studies presented at the 37th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2025), held November 3–5, 2025, in Athens, Greece. These works advance interpretable AI for digital platforms by examining how online systems shape user experience and information exposure. They examine how recommendation systems can confine users to narrow content pathways, introduce a transparent framework for identifying toxic intent in online interactions, and present a tri-modal method for extracting representative keyframes from large-scale video data. Together, they offer new ways to understand how digital content is curated, consumed, and analyzed.

The first study, “Detecting Algorithmic Homophily in Recommendation Graphs via Weighted Topic Distribution,” investigates how YouTube recommendation systems reinforce topical similarity, creating “content traps.” By combining graph-based analysis with weighted topic modeling, it more precisely identifies a network of recommended videos acting as traps. Incorporating content with network structure in trap detection reduces false positives compared with earlier methods, improving system auditability.

The second study, “KEYS: Keyframe Extraction and Yielding Summaries,” tackles a key challenge in large-scale video analysis: selecting a small but representative set of frames for indexing, retrieval, and summarization. Using a tri-modal framework that integrates visual, semantic, and contextual signals, the method enhances the identification of meaningful keyframes and enables more efficient organization of large multimedia collections.

The third study, “Learning Hierarchical Moral Foundations for Interpretable Toxic Intent Classification via Weighted Probabilistic Soft Logic,” focuses on explainable content moderation. It combines Moral Foundations Theory with probabilistic logic to classify toxic intent using transparent rules, demonstrating strong performance across 1.27 million high-stakes online conversations while revealing how moral dimensions such as authority and care contribute to harmful discourse.

Together, these studies emphasize a shared goal: making AI systems more interpretable while applying diverse methodologies across recommendation analysis, multimedia understanding, and computational ethics. They demonstrate how AI can move beyond prediction toward explanation in various application domains, such as auditing algorithmic bias, organizing vast video data, and clarifying the moral dimensions of toxic/hate speech interactions.

Collectively, this work reflects COSMOS’s mission to design AI systems that are robust, scalable, and responsive to the complexities of digital ecosystems. For science, it advances interpretable machine learning and hybrid approaches that blend statistical and symbolic reasoning; for society, it promotes transparency, reduces bias, and supports more accountable digital platforms.